Month: December 2017

GPU growth and the compute changeover

Posted on by Ray in Artificial Intelligence, Distributed computing, Energy efficiency, Machine Learning, Market dynamics, Neural network, Processing performance, Strategic Inflection Points, System effectiveness

Attended SC17 last month in Denver and Nvidia had almost as big a presence as Intel. Their VR display was very nice as compared to some of the others at the show.

GPU past

GPU’s were originally designed to support visualization and the computation to render a specific scene quickly and efficiently. In order to do this they were designed with 100s to now 1000s of arithmetically intensive (floating point) compute engines where each engine could be given an individual pixel or segment of an image and compute all the light rays and visual aspects pertinent to that scene in a very short amount of time. This created a quick and efficient multi-core engine to render textures and map polygons of an image.

Image rendering required highly parallel computations and as such more compute engines meant faster scene throughput. This led to todays GPUs that have 1000s of cores. In contrast, standard microprocessor CPUs have 10-60 compute cores today.

GPUs today 

Funny thing, there are lots of other applications for many core engines. For example, GPUs also have a place to play in the development and mining of crypto currencies because of their ability to perform many cryptographic operations a second, all in parallel

Another significant driver of GPU sales and usage today seems to be AI, especially machine learning. For instance, at SC17, visual image recognition was on display at dozens of booths besides Intel and Nvidia. Such image recognition  AI requires a lot of floating point computation to perform well.

I saw one article that said GPUs can speed up Machine Learning (ML) by a factor of 250 over standard CPUs. There’s a highly entertaining video clip at the bottom of the Nvidia post that shows how parallel compute works as compared to standard CPUs.

GPU’s play an important role in speech recognition and image recognition (through ML) as well. So we find that they are being used in self-driving cars, face recognition, and other image processing/speech recognition tasks.

The latest Apple X iPhone has a Neural Engine which my best guess is just another version of a GPU. And the iPhone 8 has a custom GPU.

Tesla is also working on a custom AI engine for its self driving cars.

So, over time, GPUs will have an increasing role to play in the future of AI and crypto currency and as always, image rendering.

 

Photo Credit(s): SC17 logo, SC17 website;

GTX1070(GP104) vs. GTX1060(GP106) by Fritzchens Fritz;

Intel 2nd Generation core microprocessor codenamed Sandy Bridge wafer by Intel Free Press

Quantum computer programming

Posted on by Ray in Executive leadership, R&D measures, Strategic Inflection Points

I was on a vendor call last week and they were discussing their recent technological advances in quantum computing. During the discussion they mentioned a number of ways to code for quantum computers. The currently most popular one is based on the QIS (Quantum Information Software) Kit.

I went looking for a principle of operations on quantum computers. Ssomething akin to the System 360 Principles of Operations Manual that explained how to code for an IBM 360 computer. But there was no such manual.

Instead there is a paper, on the Open Quantum Assembly Language (QASM) that describes the Quantum computational environment and coding language used in QIS Kit.

It appears that quantum computers can be considered a special computational co-proccesor engine, operated in parallel with normal digital computation. This co-processor happens to provide a quantum simulation.

QASM coding

One programs a quantum computer by creating a digital program which describes a quantum circuit that uses qubits and quantum registers to perform some algorithm on those circuits. The quantum circuit can be measured to provide a result  which more digital code can interpret and potentially use to create other quantum circuits in a sort of loop.

There are four phases during the processing of a QIS Kit quantum algorithm.

  1. QASM compilation which occurs solely on a digital computer. QASM source code describing the quantum circuit together with compile time parameters are translated into a quantum PLUS digital intermediate representation.
  2. Circuit generation, which also occurs on a digital computer with access to the quantum co-processor. The intermediate language compiled above is combined with other parameters (available from the quantum computer environment) and together these are translated into specific quantum building blocks (circuits) and some classical digital code needed and used during quantum circuit execution.
  3. Execution, which takes place solely on the quantum computer. The system takes as input, the collection of quantum circuits defined above and runtime control parameters,and transforms these using a high-level quantum computer controller into low-level, real time instructions for the quantum computer building the quantum circuits. These are then executed and the results of the quantum circuit(s) execution creates a result stream (measurements) that can be passed back to the digital program for further  processing
  4. Post-Processing, which takes place on a digital computer and uses the results from the quantum circuit(s) execution and other intermediate results and processes these to either generate follow-on quantum circuits or output ae final result for the quantum algorithm.

As qubit coherence only last for a short while, so results from one execution of a quantum circuit cannot be passed directly to another execution of quantum circuits.  Thus these results have to be passed through some digital computations before they can be used in subsequent quantum circuits. A qubit is a quantum bit.

Quantum circuits don’t offer any branching as such.

Quantum circuits

The only storage for QASM are classical (digital) registers (creg) and quantum registers (qreg) which are an array of bits and qubits respectively.

There are limited number of built-in quantum operations that can be performed on qregs and qubits. One described in the QASM paper noted above is the CNOT   operation, which flips a qubit, i.e., CNOT alb will flip a qubit in b, iff a corresponding qubit in a is on.

Quantum circuits are made up of one or more gate(s). Gates are invoked with a set of variable parameter names and quantum arguments (qargs). QASM gates can be construed as macros that are expanded at runtime. Gates are essentially lists of unitary quantum subroutines (other gate invocations), builtin quantum functions or barrier statements that are executed in sequence and operate on the input quantum argument (qargs) used in the gate invocation.

Opaque gates are quantum gates whose circuits (code) have yet to be defined. Opaque gates have a physical implementation may yet be possible but whose definition is undefined. Essentially these operate as place holders to be defined in a subsequent circuit execution or perhaps something the quantum circuit creates in real time depending on gate execution (not really sure how this would work).

In addition to builtin quantum operations,  there are other statements like the measure  or  reset statement. The reset statement sets a qubit or qreg qubits to 0. The measure statement copies the state of a qubit or qreg into a digital bit or creg (digital register).

There is one conditional command in QASM, the If statement. The if statement can compare a creg against an integer and if equal execute a quantum operation. There is one “decision”  creg, used as an integer.  By using IF statements one can essentially construct a case statement in normal coding logic to execute quantum (circuits) blocks.

Quantum logic within a gate can be optimized during the compilation phase so that they may not be executed (e.g., if the same operation occurs twice in a gate, normally the 2nd execution would be optimized out) unless a barrier statement is encountered which prevents optimization.

Quantum computer cloud

In 2016, IBM started offering quantum computers in its BlueMix cloud through the IBM Quantum (Q)  Experience. The IBM Q Experience currently allows researchers access to 5- and 16-qubit quantum computers.

There are three pools of quantum computers: 1 pool called IBMQX5, consists of 8 16-qubit computers and 2 pools of 5 5-qubit computers, IBMQX2 and IBMQX4. As I’m writing this, IBMQX5 and IBMQX2 are offline for maintenance but IBMQX4 is active.

Google has recently released the OpenFermion as open source, which is another software development kit for quantum computation (will review this in another post). Although Google also seems to have quantum computers and has provided researchers access to them, I couldn’t find much documentation on their quantum computers.

Two other companies are working on quantum computation: D-Wave Systems and Rigetti Computing. Rigetti has their Forest 1.0 quantum computing full stack programming and execution environment but I couldn’t easily find anything on D-Wave Systems programming environment.

Last month, IBM announced they have  constructed a 50-Qubit quantum computer prototype.

IBM has also released 20-Qubit quantum computers for customer use and plans to offer the new 50-Qubit computers to customers in the future.

Comments?

Picture Credit(s): Quantum Leap Supercomputer,  IBM What is Quantum Computing Website

QASM control flow, Open Quantum Assembly Language, by A. Cross, et al. 

IBM’s newly revealed 50-Qubit Quantum Processer …,  Softcares blog post

Blockchain, open source and trusted data lead to better SDG impacts

Posted on by Ray in Data integrity, Distributed computing, Information economy, Mobile computing, System effectiveness, Visionary leadershp

Read an article today in Bitcoin magazine IXO Foundation: A blockchain based response to UN call for [better] data which discusses how the UN can use blockchains to improve their development projects.

The UN introduced the 17 Global Goals for Sustainable Development (SDG) to be achieved in the world by 2030. The previous 8 Millennial Development Goals (MDG) expire this year.

Although significant progress has been made on the MDGs, one ongoing determent to  MDG attainment has been that progress has been very uneven, “with the poorest and economically disadvantaged often bypassed”.  (See WEF, What are Sustainable Development Goals).

Throughout the UN 17 SDG, the underlying objective is to end global poverty  in a sustainable way.

Impact claims

In the past organizations performing services for the UN under the MDG mandate, indicated they were performing work toward the goals by stating, for example, that they planted 1K acres of trees, taught 2K underage children or distributed 20 tons of food aid.

The problem with such organizational claims is they were left mostly unverified. So the UN, NGOs and other charities funding these projects were dependent on trusting the delivering organization to tell the truth about what they were doing on the ground.

However, impact claims such as these can be independently validated and by doing so the UN and other funding agencies can determine if their money is being spent properly.

Proving impact

Proofs of Impact Claims can be done by an automated bot, an independent evaluator or some combination of the two . For instance, a bot could be used to analyze periodic satellite imagery to determine whether 1K acres of trees were actually planted or not; an independent evaluator can determine if 2K students are attending class or not, and both bots and evaluators can determine if 20 tons of food aid has been distributed or not.

Such Proofs of Impact Claims then become a important check on what organizations performing services are actually doing.  With over $1T spent every year on UN’s SDG activities, understanding which organizations actually perform the work and which don’t is a major step towards optimizing the SDG process. But for Impact Claims and Proofs of Impact Claims to provide such feedback but they must be adequately traced back to identified parties, certified as trustworthy and be widely available.

The ixo Foundation

The ixo Foundation is using open source, smart contract blockchains, personalized data privacy, and other technologies in the ixo Protocol for UN and other organizations to use to manage and provide trustworthy data on SDG projects from start to completion.

Trustworthy data seems a great application for blockchain technology. Blockchains have a number of features used to create trusted data:

  1. Any impact claim and proofs of impacts become inherently immutable, once entered into a blockchain.
  2. All parties to a project, funders, services and evaluators can be clearly identified and traced using the blockchain public key infrastructure.
  3. Any data can be stored in a blockchain. So, any satellite imagery used, the automated analysis bot/program used, as well as any derived analysis result could all be stored in an intelligent blockchain.
  4. Blockchain data is inherently widely available and distributed, in fact, blockchain data needs to be widely distributed in order to work properly.

 

The ixo Protocol

The ixo Protocol is a method to manage (SDG) Impact projects. It starts with 3 main participants: funding agencies, service agents and evaluation agents.

  • Funding agencies create and digitally sign new Impact Projects with pre-defined criteria to identify appropriate service  agencies which can do the work of the project and evaluation agencies which can evaluate the work being performed. Funding agencies also identify Impact Claim Template(s) for the project which identify standard ways to assess whether the project is being performed properly used by service agencies doing the work. Funding agencies also specify the evaluation criteria used by evaluation agencies to validate claims.
  • Service agencies select among the open Impact Projects whichever ones they want to perform.  As the service agencies perform the work, impact claims are created according to templates defined by funders, digitally signed, recorded and collected into an Impact Claim Set underthe IXO protocol.  For example Impact Claims could be barcode scans off of food being distributed which are digitally signed by the servicing agent and agency. Impact claims can be constructed to not hold personal identification data but still cryptographically identify the appropriate parties performing the work.
  • Evaluation agencies then take the impact claim set and perform the  evaluation process as specified by funding agencies. The evaluation insures that the Impact Claims reflect that the work is being done correctly and that the Impact Project is being executed properly. Impact claim evaluations are also digitally signed by the evaluation agency and agent(s), recorded and widely distributed.

The Impact Project definition, Impact Claim Templates, Impact Claim sets, Impact Claim Evaluations are all available worldwide, in an Global Impact Ledger and accessible to any and all funding agencies, service agencies and evaluation agencies.  At project completion, funding agencies should now have a granular record of all claims made by service agency’s agents for the project and what the evaluation agency says was actually done or not.

Such information can then be used to guide the next round of Impact Project awards to further advance the UN SDGs.

Ambly project

The Ambly Project is using the ixo Protocol to supply childhood education to underprivileged children in South Africa.

It combines mobile apps with blockchain smart contracts to replace an existing paper based school attendance system.

The mobile app is used to record attendance each day which creates an impact claim which can then be validated by evaluators to insure children are being educated and properly attending class.

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Blockchains have the potential to revolutionize financial services, provide supply chain provenance (e.g., diamonds with Blockchains at IBM), validate company to company contracts (Ethereum enters the enterprise) and now improve UN SDG attainment.

Welcome to the new blockchain world.

Photo Credit(s): What are Sustainable Development Goals, World Economic Forum;

IXO Foundation website

Ambly Project webpage